run_qwen2-7b_seq_balance.sh

set -x
    
gsm8k_train_path=/workspace/verlpy310/data/aime/train_short.parquet
gsm8k_test_path=/workspace/verlpy310/data/aime/test_short.parquet

train_files="['$gsm8k_train_path']"
test_files="['$gsm8k_test_path']"

python3 -m verl.trainer.main_ppo \
    data.train_files="$train_files" \
    data.val_files="$test_files" \
    data.train_batch_size=32 \
    data.val_batch_size=32 \
    data.max_prompt_length=1024 \
    data.max_response_length=512 \
    actor_rollout_ref.model.path=/workspace/HuggingFace-Download-Accelerator/qwen/models--Qwen--Qwen2.5-0.5B-Instruct \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=16 \
    actor_rollout_ref.actor.use_dynamic_bsz=True \
    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \
    actor_rollout_ref.actor.fsdp_config.param_offload=False \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
    actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \
    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=24000 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=24000 \
    critic.optim.lr=1e-5 \
    critic.model.use_remove_padding=True \
    critic.model.path=/workspace/HuggingFace-Download-Accelerator/qwen/models--Qwen--Qwen2.5-0.5B-Instruct \
    critic.model.enable_gradient_checkpointing=True \
    critic.ppo_max_token_len_per_gpu=98304 \
    critic.model.fsdp_config.param_offload=False \
    critic.model.fsdp_config.optimizer_offload=False \
    algorithm.kl_ctrl.kl_coef=0.001 \
    trainer.critic_warmup=0 \
    trainer.logger=['console','wandb'] \
    trainer.project_name='verl_example_gsm8k' \
    trainer.experiment_name='ppo0222_seqlen512' \
    trainer.n_gpus_per_node=4 \
    +trainer.val_before_train=False \
    trainer.nnodes=1 \
    trainer.save_freq=-1 \
    trainer.test_freq=5 \
    trainer.total_epochs=1 $@